由药物重新定位的全球相似性增强的子图神经网络
Chengyan Zhou1,2, Xinliang Sun2, Xiang Du2,3
1School of Software, Xinjiang University, Urumqi, 830091, China.
Interdisciplinary sciences, computational life sciences
|October 30, 2025
概括
这项研究介绍了GSESNN,一种用于药物重新定位的新型图形神经网络方法. 通过分析药物疾病关系,GSESNN有效地识别了现有药物的新用途,提高了药物发现效率.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物重新定位通过为现有药物找到新的用途来加速开发并降低成本.
- 图形卷积网络 (GCN) 越来越多地用于药物重新定位,但往往无法在图形中捕获不同的节点角色.
- 现有的GCN方法可能难以学习有效的表示,因为在药物-疾病关联图中忽视了节点的重要性.
研究的目的:
- 提出一种通过全球相似性 (GSESNN) 增强的新型子图神经网络,以改善药物重新定位.
- 解决现有方法的局限性,这些方法忽视了药物疾病关联图中不同的节点角色.
- 为了提高预测药物疾病关联的准确性.
主要方法:
- GSESNN从一个更大的图中提取药物疾病对子图.
- 它使用GCN和排序聚合来学习子图表示.
- 来自GCN的全球相似性信息与子图表表示融合在一起,以区分类似的图表拓.
主要成果:
- 在药物重新定位任务中,GSESNN的表现优于基线模型.
- 该模型成功地在阿尔茨海默病和胃癌的案例研究中确定了准确的药物疾病关联.
- 实验结果证明了该模型在预测药物疾病关联方面的有效性.
结论:
- GSESNN提供了一种有前途的方法来重新定位药物,通过从药物疾病图表中有效地学习表征.
- 该模型整合全球相似性的能力增强了其预测能力.
- 在加速药物发现和开发方面,GSESNN显示了实际应用的潜力.
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